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Amazon rekognition research paper

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Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. Amazon Rekognition also provides highly accurate facial analysis and facial search capabilities that you can use to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases. With Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs.
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New media analysis features for Amazon Rekognition

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Amazon Rekognition – Pricing - AWS

We use cookies and other tracking technologies to improve your browsing experience on our site, show personalized content and targeted ads, analyze site traffic, and understand where our audiences come from. To learn more or opt-out, read our Cookie Policy. The product, called Rekognition, has been the source of controversy. Amazon is announcing a one-year moratorium on allowing law enforcement to use its controversial Rekognition facial recognition platform, the e-commerce giant said on Wednesday.
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Detecting objects and scenes

Customers often need to analyze their images to find objects that are unique to their business needs. In this post, we showcase how […]. When Software Engineer Florian Thomas describes Deliveroo, he is talking about a rapidly growing, highly in-demand company. Today we are announcing updates to our face detection, analysis, and recognition features. These updates provide customers with improvements in the ability to detect more faces from images, perform higher accuracy face matches, and obtain improved age, gender, and emotion attributes for faces in images.
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The Rekognition facial-detection technology that Amazon is marketing to law enforcement often misidentifies women, particularly those with darker skin, according to researchers from MIT and the University of Toronto. Lighter-skinned women were misidentified 7 percent of the time. Darker-skinned men had a 1 percent error rate, while lighter-skinned men had none.
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